Ecological Economics 108 (2014) 229–241

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Ecological Economics

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Analysis

Valuing conservation benefits of an offshore marine protected area

Tobias Börger a,⁎, Caroline Hattam a, Daryl Burdon b, Jonathan P. Atkins c, Melanie C. Austen a

a Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UKb Institute of Estuarine and Coastal Studies, University of Hull, Hull HU6 7RX, UKc Hull University Business School, University of Hull, Hull HU6 7RX, UK

⁎ Corresponding author.E-mail address: tobo@pml.ac.uk (T. Börger).

http://dx.doi.org/10.1016/j.ecolecon.2014.10.0060921-8009/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 May 2014Received in revised form 24 September 2014Accepted 19 October 2014Available online 8 November 2014

Keywords:Discrete choice experimentEnvironmental valuationDogger BankOffshore MPAMarine ecosystem servicesNorth Sea

Increasing anthropogenic pressure in the offshore marine environment highlights the need for improved man-agement and conservation of offshore ecosystems. This study scrutinises the applicability of a discrete choice ex-periment to value the expected benefits arising from the conservation of an offshore sandbank in UKwaters. Thevaluation scenario refers to the UK part of the Dogger Bank, in the southern North Sea, and is based on real-worldmanagement options for fisheries, wind farms andmarine protection currently under discussion for the site. It isassessed to what extent the general public perceive and value conservation benefits arising from an offshorema-rine protected area. The survey reveals support for marine conservation measures despite the general public'slimited prior knowledge of current marine planning. Results further show significant values for an increase inspecies diversity, the protection of certain charismatic species and a restriction in the spread of invasive speciesacross the site. Implications for policy andmanagement with respect to commercial fishing, wind farm construc-tion and nature conservation are discussed.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

With recent technological developments and an expanding globaleconomy, there is increasing pressure from human activities on the off-shore environment (Stojanovic and Farmer, 2013). Offshore waters aretypically considered to be those outside territorial waters (i.e. beyond12 nautical miles from the coast, including international waters) andhave traditionally been exploited by the commercial fishing, oil and gas,and aggregate sectors. However, the expanding blue economy, which isconsidered to offer “smart, sustainable and inclusive economic and em-ployment growth from the oceans, seas and coasts” (EC, 2012), exem-plifies a growing interest in the offshore marine environment. It reflectsa wider range of activities including: marine energy extraction; aquacul-ture;maritime, coastal and cruise tourism;marinemineral resources; andblue biotechnology (DGMARE, 2012; EC, 2012). With the resulting pres-sures on offshore sites, the challenges for the management of remoteparts of themarine environment become far greater. These developmentsrequire more informed planning and management to ensure sustainableuse of offshore marine resources. While marine planning has been effec-tive in reaching out to particular user groups, such planning is increasing-ly required by legislation to engage all stakeholders including non-usergroups such as the general public (cf. EC, 2001; Jobstvogt et al., 2014).This demand for wider engagement recognises that the marine environ-ment provides ecosystem services which are fundamental to humanwell-being (Beaumont et al., 2007; Atkins et al., 2011; Börger et al., 2014).

While the economic benefits of offshore activities such as fisheriesare widely discussed (e.g. Sumaila et al., 2007), evidence on the widersocietal benefits of themarine environment, in which these offshore ac-tivities take place, is lesswell known (Pendleton et al., 2007; Armstronget al., 2010). Few published studies have explored the benefits providedby the offshore marine environment (Glenn et al., 2010; McVittie andMoran, 2010; Ressurreição et al., 2011, 2012; Jobstvogt et al., 2014).Much greater attention has focused on coastal areas (e.g. Atkins et al.,2007; Eggert and Olsson, 2009; Taylor and Longo, 2010; Hynes et al.,2013). Other studies have used benefit transfer to value the benefits ofoffshore sites (Hussain et al., 2010; Londoño and Johnston, 2012), butare limited by the scarcity of primary valuation data. Environmental im-pact assessments associated with the creation of wind farms or the des-ignation of marine protected areas (MPAs) are starting to increase theoffshore evidence base (e.g. Talisman Energy, 2006; Defra, 2012), butvaluation evidence still remains limited. This paper focuses on gaininggreater insight into how the general public perceive and value the ben-efits of the offshore marine environment. For this purpose a case studyassessment is made employing a discrete choice experiment (DCE) tovalue benefits provided by the conservation of an offshore sandbankunder UK jurisdiction.

1.1. Management of an Offshore MPA: The Dogger Bank

The Dogger Bank is the largest offshore sandbank in the North Sea,covering a total area of approximately 17,600 km2 (Diesing et al.,2009) (Fig. 1). The waters surrounding the Dogger Bank are relativelyshallow (Diesing et al., 2009), are highly productive (Kröncke, 2011),

Fig. 1. Location of the Dogger Bank (the dotted outline shows the area under UK jurisdiction).

230 T. Börger et al. / Ecological Economics 108 (2014) 229–241

and support an important seabed community (Wieking and Kröncke,2003; Kröncke, 2011)which provides a vital food resource for the diversefish assemblage (Sell and Kröncke, 2013). The Dogger Bank is also an eco-nomically important area. Historically it has been used as a rich fishingground primarily by Dutch, German, Danish and UK fleets, and competi-tion for space is intense and increasing. This area of the southern NorthSea has further been used for natural gas exploration with five platformsstill present across the site (JNCC, 2011). There are also two small areaswhich are licensed for aggregate extraction (JNCC, 2011). More recently,plans have been submitted for the development of the largest windfarm in Europe on the UK sector of the Dogger Bank, covering an area of8660 km2 (Forewind, 2010). Given the shallow depth of water foundabove the Dogger Bank, very little commercial shipping takes place(JNCC, 2011), however thewrecks found on and around the Bank providefor a limited amount of recreational angling and diving (Forewind, 2013).

1.2. Current Governance

The Dogger Bank is located within the exclusive economic zones(EEZ)1 of four European Union (EU) Member States (Fig. 1). Three ofthese Member States (the UK, Germany and the Netherlands) are inthe process of designating the sandbank under their jurisdiction as aSpecial Area of Conservation (SAC), a form of MPA, under the EU Habi-tats Directive (EC, 1992).Within the EU, the establishment of a networkofMPAs is driven by both international obligations (e.g. the OSPAR Con-vention in theNorth East Atlantic) and the implementation of European

1 An exclusive economic zone (EEZ) is defined by theUnitedNations Convention on theLawof the Sea (UNCLOS, 1982) as an area beyond and adjacent to the territorial sea, underwhich the rights and jurisdiction of the coastal State and the rights and freedoms of otherStates are governed by the relevant provisions of the UNCLOS.

Directives (e.g. Good Environmental Status under the Marine StrategyFramework Directive (2008/56/EC) and Favourable Conservation Statusfor habitats and species under the Habitats Directive (92/43/EEC) andBirds Directive (2009/147/EC) (Potts et al., 2014).

In the UK, the Joint Nature Conservation Committee (JNCC) is re-sponsible for the identification of SACs in offshore marine waters fordesignation by the Government. The JNCC is therefore responsible forestablishing conservation objectives for offshore SACs. The DoggerBank was recommended by JNCC to the UK Government as a candidateSAC (cSAC) for the protection of ‘sandbanks which are slightly coveredby seawater all the time’ (EC, 1992). Following a baseline assessment,JNCC reported the Dogger Bank as being in unfavourable conservationstatus. The site is vulnerable to physical disturbance or abrasion (e.g.from bottom trawl fishing gear2 and pipeline burial), selective extrac-tion of species (e.g. by bottom trawl fishing), and physical loss by ob-struction (e.g. permanent construction of wind farms) (JNCC, 2011).Its vulnerability to the introduction of non-native species has not beenpossible to asses (JNCC, 2011). To restore the site and achieve favourableconservation status, the establishment of a management plan is re-quired to provide the basis for determiningwhat current or future activ-ities may have an impact on the overall health of the site.

To develop such a management plan, the UK, Germany and theNetherlands formed the transnational Dogger Bank Steering Group(DBSG), in which the European Commission and Denmark have an ob-server status. In 2011, theDBSG invited theNorth Sea Regional AdvisoryCouncil (NS RAC), which consists mainly of representatives fromthe fisheries and wind farm industries and environmental non-governmental organisations (ENGOs), to develop a proposal for a

2 Bottom trawl fishing, also known as demersal fishing, involves the towing of heavy fish-ing gear along the seabed.

231T. Börger et al. / Ecological Economics 108 (2014) 229–241

fisheries management plan for the Dogger Bank (NS RAC, 2012). Thisnovel bottom-up decision-making process was welcomed by moststakeholders and policy-makers. The process eventually stalled, howev-er, because fishing representatives and ENGOs were unable to adopt acompromise proposal regarding habitat protection (NS RAC, 2012).This situation results in part from the lack of evidence on species, habi-tats and fisheries impacts on the Dogger Bank, and on what percentageof the Dogger Bankwould need to be protected to achieve the conserva-tion objectives set for the respective cSACs.

Based on the above considerations, the main purpose of this study isto test the applicability of theDCEmethodology to the valuationof the ex-pected benefits of an offshore site as a result of management options cur-rently under discussion. More specifically, the research objectives are to:

1. Assess the general public's level of concern regarding the environ-mental consequences of offshoremarine activities and potential sup-port for marine management plans.

2. Assess the social benefits of proposed management plans to an off-shore sandbank (specifically the Dogger Bank).

3. Determine the value of the potential outcomes of management plansthat target specific aspects of biodiversity (both positive and negativeattributes, such as species diversity, charismatic species and invasivespecies).

4. Identify determinants of respondents' preferences for different as-pects of the proposed management plans.

Consequently, this paper contributes to the literature on the valua-tion of the offshore marine environment and, in particular, ecosystemservices associated with protected offshore sandbanks. The remainderof the paper is structured as follows: Section 2 describes the DCE ap-proach and survey development; Section 3 presents the results; andSection 4 provides the discussion and policy implications.

2. Methodology

2.1. Discrete Choice Experiments (DCEs)

Besides the contingent valuation method (CVM), discrete choice ex-periments (DCEs) (Hanley et al., 1998; Louviere et al., 2000) are one ofthemost popular approaches to assess the total economic value (TEV) ofnon-market environmental goods. These methods use surveys to elicitthe willingness-to-pay (WTP) of respondents for securing some futurepositive environmental change (or to prevent some negative changefrom happening). WTP estimates are interpreted as indicators of thechange in utility that respondents expect from the specific environmen-tal change. Aggregated over the representative sample of the affectedpopulation, WTP estimates provide quantification of the benefits of en-vironmental changes for that population, e.g. for users and non-users ofa particular area.3

Few non-coastal marine valuation studies have used quantifiable at-tributes. Glenn et al. (2010) and Wattage et al. (2011) report on a DCEon MPAs around cold-water coral reefs in Irish waters but do not pro-vide any monetary values for the protection of corals or the banningof fishing activities. The CVM study in Ressurreição et al. (2011, 2012)reports significantly positive WTP of respondents in the Azores for thepreservation of species diversity in waters around the archipelago.McVittie and Moran (2010) conducted a DCE survey to value the provi-sion of ecosystem services resulting from the network of UK marineconservation zones. While these authors base their valuation scenariosentirely on a real-world management measure (the UK Marine Bill),the attribute levels are described qualitatively. This makes it very diffi-cult to link changes in the underlying ecological indicators to the elicitedvalues. The only non-coastal DCE study reporting WTP for the

3 Alternatively, the willingness to accept (WTA) compensation to forgo a positivechange or to accept a negative change can be assessed. Most applications use the WTPconcept.

protection of deep-sea biodiversity and using quantifiable attributes isJobstvogt et al. (2014). Their survey elicits WTP of Scottish householdsfor additional MPAs in the Scottish deep-sea. They focus on two valuecategories of biodiversity: the existence value for deep-sea species andthe option value of deep-sea organisms as a source of future medicinalproducts. Given the scarcity of evidence in this field, the present studyimplements a DCE that is firmly linked to real-worldmanagementmea-sures for offshoremarine sites. It also uses quantifiable attributes, whichcan be directly linked to measurable changes resulting from the pro-posed management. The use of clearly quantifiable attributes makesresulting value estimates suitable for benefit transfer (Loomis andRosenberger, 2006; Richardson et al., 2014).

2.2. Development of Valuation Scenarios and Choice Attributes

The development of valuation scenarios was based upon the discus-sions of the DBSG regarding the management of the Dogger Bank. Therange of management proposals brought forward by the differentparties leave considerable scope for the development of relevant choiceattributes. At the same time attributes represented certain ecosystemservice categories. The two sectors likely responsible for the greatestcurrent and potential future impact on the Dogger Bank are commercialfishing and offshore wind farm development (JNCC, 2011). Fishing reg-ulations could affect a multitude of ecosystem services associated withthe conservation of biodiversity, in addition to food provision. For exam-ple, theDogger Bankhas historically been important for sandeels (Cefas,2007). They are important prey for other commercial fish species (e.g.whiting, plaice, mackerel and cod), seabirds (e.g. fulmar and kittiwake)and cetaceans, especially the harbour porpoise (Diesing et al., 2009).Prohibiting bottom trawling activities on part of the Dogger Bankwould conserve the sandeel populations, as well as improve the healthand diversity of the wider marine community (Kaiser et al., 2006;Olsgard et al., 2008). Seabed communities are also known to play arole in a number of regulating ecosystem services (Snelgrove, 1999;Austen et al., 2011), such as carbon sequestration and storage and theregulation of waste products. They also provide supporting services in-cluding nursery grounds for commercially important fish species. TheDogger Bank acts as a nursery ground for plaice, providing suitable hab-itat for foraging and maturing fish (Diesing et al., 2009; Hufnagl et al.,2013). In addition, by excluding fishing activities on part of the DoggerBank, porpoises, seals and seabirds, charismatic species with culturalsignificance, are less likely to be caught as by-catch in the area(Vinther and Larsen, 2004; Zydelis et al., 2009; Sonntag et al., 2012;Brown et al., 2013). Changes in species diversity and protection of por-poises, seals and seabirds were therefore selected as choice attributes(Table 1). Recognising that a species is rare or endangered may lead torespondents stating a higher WTP for their protection (Christie et al.,2006), the conservation status of porpoises, seals and seabirds wastherefore not mentioned.

The attribute levels for species diversity and protection of porpoises,seals and seabirds were chosen based on the areas for closure proposedby the different stakeholders for the Dogger Bank and scaled to the UKsection. The ENGOs and Germany are seeking closure of 50% of thearea to all fishing activities, while the fishing industry would prefer amaximum of 25% of the area to be closed. Taking these as attributelevels, under the conservation scenario porpoises, seals and seabirdswould be protected on 50% of the Dogger Bank by preventing potentialby-catch by the fishing industry (e.g. Brown et al., 2013), but only on25% under the fishing industry proposal. The link between the cessationof a particular type of fishing gear, namely bottom trawling, and changein species diversity is less clear. Drawing from correlations in Kaiseret al. (2006) suggests, however, that it is reasonable to assume that re-moval of trawling on 25% of the area could lead to a 10% increase in spe-cies diversity, while removal from 50% of the area could lead to anincrease in species diversity of approximately 25%.

Table 1Choice attributes (status quo in italics).

Attribute Description in the questionnaire Levels

Diversity of species Reducing or removing trawling in some parts of the Dogger Bank will:

• Increase the diversity of fish, invertebrates and other marine species• Enhance the natural functions provided by the Dogger Bank (contributing to theregulation of climate, maintenance of clean water and support of fish populations)

No change, 10% increase in species diversity, 25%increase in species diversity

Protection of porpoises,seals and seabirds

The Dogger Bank provides a natural home for porpoises and seals, and is a feedingground for seabirds.

• These animals and birds are sometimes accidentally caught in fishing nets.• The use of harmful nets will be regulated or forbidden on some parts of the DoggerBank meaning these animals will be better protected.

• Fishing vessels will not be banned from the whole area.

Not protected, protected on 25% of the Dogger Bankarea, protected on 50% of the Dogger Bank area

Invasive species The construction of wind turbines on the Dogger Bank provides space for invasivespecies, increasing the ability to spread elsewhere.

• They may affect the survival of species normally found there.• The higher the numbers of turbines and the closer they are, the greater the likeli-hood of invasive species becoming established.

Restricted spread, wide spread

Additional tax Monitoring and enforcing the Dogger Bank management plan will be costly. Thegovernment therefore needs to raise additional funds through taxes.

• The tax is payable by all households in the UK for the next 5 years.• If the overall funds people are willing to contribute do not cover the cost of moni-toring and enforcement, the plan cannot be put into action.

£0, £5, £10, £20, £30, £40, £60

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The third attribute selectedwas the spread of invasive species acrossthe North Sea as a potential outcome of the installation of offshore windturbines.Many species are extending their ranges northwards due to in-creasing sea temperatures caused by climate change (Perry et al., 2005;Tasker, 2008; Rijnsdorp et al., 2009; Edwards et al., 2013; Mieszkowskaet al., 2013). For hard substrate dwelling species the introduction of per-manent hard structures into what is a dynamic, mobile sandbank hasthe potential to provide new habitat for species that are currently un-able to colonise the locality (Petersen and Malm, 2006; Glasby et al.,2007; Bulleri and Chapman, 2010). This increases their potential to sur-vive and, with the turbines acting as stepping stones, spread throughoutthe North Sea. Once established invasive species may compete with na-tive species for resources such as food and space (e.g. Arenas et al.,2006). Invasive species are already found in the North Sea, e.g. the seawalnut (Mnemiopsis leidyi), colonial sea squirts such as Botrylloidesviolaceus, Atlantic blue crab (Callinectes sapidus), Japanese skeletonshrimp (Caprella mutica) (Galil et al., 2014). Of particular relevance inthis case are the spread of species such as the colonial sea squirt andthe Japanese skeleton shrimp which are both known to colonise artifi-cial (man-made) hard substrata in shallow waters (Arenas et al.,2006; Page et al., 2007; Turcotte and Sainte-Marie, 2009) as well asthe potential for the proliferation of those invasive jellyfish that requirehard substrate for part of their life cycle (Purcell, 2012). Consequently,the attribute levels chosen for the spread of invasive species were‘restricted’ and ‘wide spread’ across the North Sea, which reflects thefact that invasive species are already present in the North Sea.

The valuation scenarios further specify that the implementation,monitoring and enforcement of the Dogger Bank management planwill come at a cost. Marine management within the UK is the responsi-bility of the Marine Management Organisation (MMO), funded ulti-mately by the taxpayer. The payment vehicle used was therefore anincrease in annual tax for UK households over the next five years.4

This attribute was given seven levels (Table 1).

4 The payment vehicle was not specified as an increase in income tax or council tax. In-come tax is a personal tax and could not justifiably apply to a household. The generichousehold tax in the UK, the council tax, is paid to local authorities. As local authoritiesare not responsible for marine management, a generic household tax was chosen as thepayment vehicle. A five year period was proposed to fall within the reporting timeframerequired for assessing the status of all SAC sites after which management measures mayneed to be reassessed.

2.3. Development of the Survey Questionnaire and Design of Choice Tasks

To develop the survey questionnaire 29 semi-structured interviewswere conducted by the project team (an interdisciplinary team com-prisingmarine ecologists and economists)withmembers of the generalpublic. Interviews were completed in Hull (Northeast England) andPlymouth and Exeter (Southwest England) to sample respondents liv-ing close to and far from the North Sea. Based on insights from these in-terviews a preliminary choice questionnaire was tested in 19 face-to-face interviews with members of the public following a workshop onmanagement of the Dogger Bank (held in Newcastle, Northeast En-gland). Respondents were asked to think aloud while they completedthe choice tasks (Ryan et al., 2009). This helped to detect inconsistenciesand unclearwording in the scenario and attribute description. The samequestionnaire was tested online with respondents from across the UK(n = 60). Findings from both pilot surveys were used to modify thequestionnaire wording and additional pictograms were included to de-scribe the choice attributes (Fig. 2). The results from the online pilot sur-vey also informed the experimental design of the main survey.Coefficients indicating the influence of the choice attributes on choicesfrom a mixed logit model (cf. next section) were used as priors whengenerating a Bayesian D-efficient design (Scarpa and Rose, 2008) inthe software package Ngene (ChoiceMetrics, 2012). Unrealistic surveyscenarios were excluded, such as choice options which yield the statusquo for each attribute at positive cost, since these options would bedominated by the status quo. The final set included 24 choice tasks sep-arated into four blocks of six tasks. Respondents were randomly allocat-ed to one block and complete the six choice tasks in that respectiveblock. Each choice task contained two alternative management plansat different cost levels and a ‘no change’ or status quo option at zerocost (Fig. 2).

The final questionnaire consisted of four parts: part one contains 19questions regarding the respondent's general knowledge of and experi-ence with the North Sea and the Dogger Bank. Part two introduced thevaluation scenarios, the hypothetical ‘Dogger Bank Management Plan’,and included the description of the choice attributes and the paymentmethod (Table 1). Part three contained the actual choice experiment.Following each choice task, respondents were asked to indicate howcertain they were of their choice on a 5-point scale from 1 “Not certainat all” to 5 “100% certain”. Part four contained a series of attitudinalquestions, some of which are used to identify protest respondents.These are respondents who chose the costless status quo option in all

Fig. 2. Example choice card from the online questionnaire.

233T. Börger et al. / Ecological Economics 108 (2014) 229–241

six choice tasks and agreed to the statements “Taxes and fees are al-ready too high, so there should be no additional financial burden”, “I al-ready pay enough for other things”, “It is my right to have a wellpreserved Dogger Bank and I should not have to pay extra for it” and“The government should cut public spending on other things insteadof expecting a contribution from me”. Answers to these questionswere given on a 5-point Likert scale from “Strongly disagree” to “Strong-ly agree”. In the regression models, dummy variables indicating agree-ment (i.e. “Agree” or “Strongly agree”) are used. The questionnaireconcluded with a series of socio-demographic questions. Three-digitpostcode data (e.g. HU6, PL1) were collected to generate distance vari-ables from the Dogger Bank, the North Sea coast and the nearest coast-line using geographical information systems (GIS). Additional dummyvariables indicating respondents that live within 1, 2, 4, 6, 10, 25 or50 miles from a coastline compared to respondents living further inlandwere computed from these data. These are used to test for distance ef-fects of elicited values.

The survey was conducted online by a market research companywith respondents sampled from their existing respondent panel.While online surveys allow researchers to reach a broad survey samplethey systematically exclude people without internet connection.5 How-ever, theproblemof hard-to-reach groups also exists for alternative sur-vey modes, such as mail or direct interviews. Regarding statedpreference valuation techniques it has been shown that online surveys

5 In the UK, 83% of households had internet access (ONS, 2013).

do not produce significantly different WTP estimates compared totraditional mail surveys (Olsen, 2009; Windle and Rolfe, 2011). Empir-ical comparisons of WTP estimates of online and face-to-face surveysshow mixed results (Canavari et al., 2005; Marta-Pedroso et al., 2007;Nielsen, 2011), but coverage of a large sampling area at justifiablecosts can best be achieved by an online survey. The survey aimed to elic-it views from a representative sample of the resident population of theUK, in terms of age, gender, and income, to assess potential non-usevalues held by respondents living far away from the actual study site.Sample characteristics are presented in the Results section.

2.4. Econometric Analysis of Choice Data

The choice data are analysed employing conditional andmixed logitmodels (Train, 2009), which are based on the random utility model(RUM) (McFadden, 1974; Ben-Akiva and Lerman, 1985). It is assumedthat choosing option j out of a set of options i = 1, …, j, …, J in choicesituation t generates utility

Unjt ¼ β0nxn jt þ εn jt ð1Þ

for respondent n. Unjt is the indirect utility of the respondent, xnjt a vec-tor of attribute levels of option j and respondent characteristics. βn de-notes a coefficient vector, some elements of which are assumed to berandom and thus respondent-specific in the mixed logit, but assumedto be fixed across respondents in the conditional logit model. The unob-servable component of utility, εnjt, is assumed to follow a type I extreme

Table 2Means, standard deviation and range of several socio-demographic variables (N = 1022).

Survey sample UK population

Mean/share (std. dev.)

Male (share) 48.6% 49.1%a

Age 18–24 (share) 12.0% 18.1%Age 25–34 (share) 17.0% 17.3%Age 35–44 (share) 17.7% 17.1%Age 45–54 (share) 17.6% 18.8%Age 55–64 (share) 14.9% 14.4%Age 65 and above (share) 20.9% 14.3%Degree or higher (share) 32.8% 27.0%c

Employed full-time (share) 39.4% 38.5%c

Employed part-time (share) 11.3% 13.7%c

Self-employed (share) 5.3% 9.5%c

Unemployed (share) 3.9% 4.4%c

Retired (share) 17.6% 13.9%c

Students (share) 9.9% 9.3%c

Household size (members) 2.64 (1.29) 2.37b

Monthly household income (£)d 2275.93 (1918.31) 2691.67b

Sources:a UK Office of National Statistics (ONS) population projections 2012-based.b ONS Labour Force Survey 2013.c ONS 2011 Census.d Income after tax and benefits.

234 T. Börger et al. / Ecological Economics 108 (2014) 229–241

value distribution, so that the probability Pnit that respondent n choosesalternative i over all other alternatives j ≠ i in choice situation t is

Pnit ¼exp β0

nxnit� �

X Jj¼1

exp β0nxn jt

� � : ð2Þ

Both conditional and mixed logit models produce estimates of thecoefficient vector β. The elements of β can be interpreted as the averageutility weights of the attributes included in the choice tasks (i.e. the in-fluence of these attributes on stated choices). Both models allow for theanalysis of preference variation across respondents. If different respon-dents have different preferences for a certain attribute, respondent-specific variables can be interacted with attribute-specific variables toaccount for these different preferences (Train, 2009). As the conditionallogit cannot account for unobserved preference heterogeneity, the anal-ysis presented here relies heavily on the mixed logit model. By specify-ing the distribution of the elements of the coefficient vector β, the lattermodel can also account for random (i.e. unexplained) preference het-erogeneity. The coefficients of all but the cost attribute are assumed tobe normally distributed, so bothmeans and standard deviations of coef-ficients of the respective attributes are reported.6 Respondent-specificvariables are included in the models in interactions with the attributevariables or as a dummy variable indicating whether the status quo orone of the policy options was chosen. Both models produce coefficientestimates through maximum likelihood estimation. This study usesthe asclogit command in Stata (version 12) to compute the conditionallogit model. The mixed logit model is estimated using the user-written command developed by Hole (2007).

The dependent variable in all of the above models is the choice thatrespondents make between the offered alternatives, so the coefficientvector β reports the influence of explanatory variables on choice proba-bilities. As the choice experiment includes a cost component, WTP forattribute k can be calculated as

WTPk ¼ − βk

βcostð3Þ

where βk and βcost denote the coefficients of the k-th attribute and of thecost attribute, respectively. When the mixed logit model is used βk rep-resents the mean of the distribution of the coefficient of the k -thattribute.

3. Results

3.1. Sample Characteristics

The main survey was conducted in early December 2013. In total1022 respondents completed the questionnaire. To check whether thesample reflects the structure of the UK population, sample characteris-tics are compared against population means. Shares of age groups re-flect age distribution in the population (Table 2) except for a slightoversampling of respondents aged 65 and over at the expense of the18–24 and over group. The level of education is somewhat higher inthe sample than in the general population. As an education indicatorthe share of people with at least a university degree is 32.8% in thesample but only 27.0% in the general population. The distribution of oc-cupations closely resembles that of the general UK population. Self-employed and retired respondents are slightly underrepresented com-pared to the 2011 census figures. Average household size in the sampleis 2.64 persons per household compared to 2.37 for the whole country.

6 It is possible to assume other distributions for the choice attribute coefficients, such asuniformor log-normal, but in this study it is not clear a prioriwhich sign of the coefficientscan be expected, so we decided to apply the most commonly used normal distribution.Since, on the contrary, it can be expected that the cost coefficient will be negative, it is as-sumed to be fixed across respondents.

The mean monthly household income of £2275 is lower than averagehousehold income for thewhole of the UK. Based on these comparisons,the survey samplewas considered to reflect the structure of theUKpop-ulation. The survey findings therefore have significance for the residentpopulation as a whole and can be used in cost-benefit analysis (CBA).

In terms of spatial distribution of the sample across the UK, Fig. 3shows that respondents from almost all postcode areas of the countrywere sampled (115 out of 125).

3.2. Knowledge About and Attitudes Towards the Use and Management ofOffshore Sites

70.6% of respondents stated that they had previously visited theNorth Sea coast. About half the respondents had taken a ferry (49.9%)or flight (63.0%) across the North Sea. Themore specific topic of the sur-vey, the Dogger Bank, was new to most respondents. Only 50.2% of therespondents stated that they had heard of the Dogger Bank before com-pleting thequestionnaire,with the vastmajority of them(80.3%) havingheard of it in the shipping forecast.7 After further introduction to thesite, only 3.9% of respondents stated that they were previously awareof all the information presented, and 37.0% stated they were aware ofsome of it. The remaining 59.1% stated that they were unaware of thisinformation before undertaking the survey. Similarly, regarding MPAs,only 19.9% of respondents had heard of the UK government's plans tocreate a network of MPAs by 2016.

Part one of the questionnaire also elicited the level of support for thetwo main components of the hypothetical Dogger Bank managementplan: regulation of fisheries and futurewind farm development. Despitethe low level of existing knowledge, the majority of respondents sup-ported regulations both of fisheries and future wind farm developmentin that area (Table 3). Only a small proportion of respondents directlyrejected the plans to regulatefishing andwind farmdevelopment intro-duced in the survey scenarios (3.8% and 6.5% respectively).

3.3. WTP for Environmental Benefits From the Offshore Marine Site

For the analysis of choice data, 49 respondents were identified asprotestors based on responses to attitudinal questions and were

7 The shipping forecast is a national institution in the UK. It is a weather forecast for theseas around the British Isles prepared by the Met Office on behalf of the Maritime andCoastguard Agency. It is broadcast four times a day on BBC Radio 4 and published online.

Fig. 3. Spatial distribution of survey responses and location of the Dogger Bank.

235T. Börger et al. / Ecological Economics 108 (2014) 229–241

subsequently removed from the survey sample.8 The variables used inall subsequent models and their description are listed in Table 4. Inboth the conditional and mixed logit models reported in Tables 5a and5b, coefficients of all attributes were significant and exhibit the expect-ed signs. Changes in species diversity and the protection of charismaticspecies positively influenced choices (i.e. on average, choice options de-scribing an increase of these attributes had a higher probability of beingpreferred than those indicating no change). In contrast, the coefficientsfor wide spread of invasive species (INVASIVE) and the cost attributewere significantly negative in bothmodels. The higher the cost of an op-tion in terms of the household tax the smaller the probability that thisoption was selected.

Tables 5a and 5b report WTP estimates computed from the condi-tional and mixed logit models according to Eq. (3). There was no clearpattern that eithermodel produces only higher or lowerWTP estimates.The relative importance of the attributes as measured in WTP did not

8 A share of protesters of about 5% is relatively low. Therefore, we ran additional choicemodels with samples, fromwhich up to 20% of respondentswere removed based on somestricter identification rules. This had very little effect on the coefficient estimates. Giventhat there is no universal agreement as to the identification of protesters, we decided totake a precautionary approach and keep the number of protest removals as low aspossible.

change across models. However, the mixed logit model's fit to thedata as indicated by the adjusted ρ2 and the Bayesian Information Crite-rion (BIC) was much better compared to the conditional logit model.WTP for the larger change in one attribute (e.g. 25% increase in speciesdiversity) always exceeded WTP for the smaller change (e.g. 10% in-crease in species diversity). While respondents were willing to pay£4.19 (£5.70 in the conditional logit) per year on average for a 10% in-crease in species diversity on the Dogger Bank, their average WTP fora 25% increase was £7.76 (£7.22 in the conditional logit). WTP did notappear to increase linearly with the level of this attribute, but indicateddecreasing marginal utility of species diversity. Respondents seemedwilling to pay for a certain base level of change but theirWTP did not in-crease proportionately for further positive changes. This pattern couldalso be detected for the protection of porpoises, seals and seabirds. Re-spondents were willing to pay £24.02 (£26.24 in the conditional logit)on average per year to protect these species on 25% of the UK DoggerBank area, whereas the WTP for the protection on 50% of the area was£30.32 (£33.07 in the conditional logit). Designing a future wind farmon the Dogger Bank that results in wide spread of invasive speciesyielded a negative WTP of £−25.39 (£−22.93 in the conditional logit)on average to respondents in this sample. These estimates can beinterpreted as an indicator of a negative welfare effect resulting from adecrease in environmental quality as indicated by this attribute.

Table 3Number of respondents supporting or rejecting management measures for the Dogger Bank SAC.

Do you think fishing activities should be regulated torestore the environmental functions of the DoggerBank?

Do you think wind farm design should be regulated toprevent further introduction of invasive species to theDogger Bank?

Number of respondents (N) Share Number of respondents (N) Share

No 39 3.8% 66 6.5%Yes 654 64.0% 632 61.8%I need more information 253 24.8% 229 22.4%Don't know 76 7.4% 95 9.3%Total 1022 1022

236 T. Börger et al. / Ecological Economics 108 (2014) 229–241

Relating these annual monetary figures to reported monthly householdincome (after tax and benefits), they make up shares between 0.018%and 0.112%.

As themixed logit model relaxes the assumption that attribute coef-ficients are fixed across respondents, the reported coefficients weremeans of the estimated distribution. An estimate of the standard devia-tion for each coefficient is reported in Table 5b. While there was no ev-idence of random preference variation for the coefficient of theprotection of charismatic species on 25% of the Dogger Bank(PROT25), all other attribute coefficients modelled had standard devia-tions significantly different from zero. These results provide evidence of

Table 4Description of variables used in the regression models (N = 973).

Variable Description

Variables specific to the choice alternativeASC_CHANGE Alternative-specific constant (0 = no-change option,

1 = management plan B or C)SPEC10 Increase in species diversity by 10 percenta

SPEC25 Increase in species diversity by 25 percenta

PROT25 Porpoises, seals and seabirds protected on 25 percent of the UK'sDogger Bank areaa

PROT50 Porpoises, seals and seabirds protected on 50percent of the UK'sDogger Bank areaa

INVASIVE Wide spread of invasive species on the Dogger Banka

COST Cost of the Dogger Bank management plan as additional tax forthe household in £ GBP

Variables specific to the respondentAGE Age of the respondent in yearsMALE Gender of the respondenta

UNI Respondent has got a university degreea

INCOME Monthly household income of the respondent in £ GBPHHSIZE Number of household membersENVORG Respondent is member of an environmental organisationa

NSEA Respondent lives or works within 10 miles of the North Seaa

FERRY Respondent has taken a boat or ferry trip on the North Seaa

FLIGHT Respondent has flown over the North Seaa

ANGLING Respondent has been recreational sea anglinga

SCUBA Respondent has been scuba divinga

MARSEC Respondent has previously worked in a marine sector (fisheries,offshore renewable energy or oil and gas)a

DIST_DB Distance to the Dogger Bank in kmCERTAIN Sum of six choice certainty questions, each on a 5-point scale

from 1 “Not certain at all” to 5 “100% certain”NOTNEC “I think a Dogger Bank management plan is not necessary”b

BURDEN “Taxes and fees are already too high, so there should not be anadditional financial burden”b

ENOUGH “I already pay enough for other things”b

CUT “The government should cut public spending on other thingsinstead of expecting a contribution from me”b

ENJOY “I enjoy contributing to a good cause no matter what it is”b

EXPERTS “I think it is better to ask experts whether or not to carry out thisDogger Bank management plan”b

SPECDIV “Without a management plan the diversity of species on theDogger Bank will continue to decrease”b

PROTECT “Porpoises, seals and seabirds need protecting through themanagement plan”b

a Dummy variable (1 = yes, 0 = no).b Dummy variable (1 = agree or strongly agree, 0 = strongly disagree, disagree or

indifferent).

substantial random preference heterogeneity, consequently only themixed logit model was employed to identify individual specific choicedeterminants.

3.4. Determinants of Preferences and WTP for Offshore MarineManagement

To identify determinants of choices, and thus ofWTP, and to validatethe survey responses, interactions between the management optiondummy (ASC_CHANGE) and/or certain choice attributes and a set ofrespondent-specific variables were included in the mixed logit model.The three models reported in Table 6 contain an increasing number ofexplanatory variables. Often the number of observations decreases themore explanatory variables are included in the model resulting fromthe exclusion of respondents with missing values. It should be notedhere that the number of observations remained constant across modelsdue to the very high item response rate in this survey.

Model 1 in Table 6 included choice attributes and 13 socio-demographic variables. In Table 6 the top section of the column reportsmean estimates of attribute coefficients modelled as random. All attri-bute coefficients were significant and point in the same direction as inthe basic model in Table 5b. Among the respondent-specific variables,the interaction between the ASC_CHANGE and MALE was negative, in-dicating that male respondents were less likely to choose any of thecostly management options. Respondents whoweremembers of an en-vironmental organisation (ENVORG) or had previously taken a ferry(FERRY) or flight across the North Sea (FLIGHT) had a higher WTP forcostly management options as indicated by the positive coefficients.The significant effects of MALE, ENVORG and FLIGHT were robustthroughout all models in Table 6.

The median response for each of the six choice certainty questionswas 4 on the scale from 1 “Not certain at all” to 5 “100% certain”. Themajority of respondents appeared to be confident to state their

Table 5aConditional logit model and WTP estimates.

Coefficient Std. Err. WTP (£)

ASC_CHANGE −0.476⁎⁎⁎ (0.101)SPEC10 0.227⁎⁎⁎ (0.075) 5.70SPEC25 0.288⁎⁎⁎ (0.055) 7.22PROT25 1.047⁎⁎⁎ (0.066) 26.24PROT50 1.320⁎⁎⁎ (0.066) 33.07INVASIVE −0.915⁎⁎⁎ (0.051) −22.93COST −0.040⁎⁎⁎ (0.002)Log-likelihood −5543Observations 17,514Respondents 973Adjusted ρ2 0.131BIC 11,135

Adjusted ρ2 is computed as ρ2 = 1 − (LLm − k)/LL0, where LLm and LL0 are the log-likelihoods of the full model and the intercept-only model respectively, and kthe number of parameters. Bayesian Information Criterion (BIC) is calculated asBIC = −2LLm + k ⋅ ln(N) with N denoting the number of respondents. The use of BIC ispreferred to Akaike Information Criterion because it imposes a stronger penalty on the in-clusion of more parameters in the model.⁎⁎⁎ indicate 1%-level of confidence.

Table 5bMixed logit model and WTP estimates.

Coefficient Std. Err. WTP (£)

Mean of random coefficientsASC_CHANGE 1.409*** (0.171)SPEC10 0.265** (0.110) 4.19SPEC25 0.490*** (0.084) 7.76PROT25 1.517*** (0.096) 24.02PROT50 1.915*** (0.112) 30.32INVASIVE −1.603*** (0.104) −25.39

Standard deviation of random coefficientsASC_CHANGE 2.512*** (0.123)SPEC10 0.578*** (0.230)SPEC25 0.815*** (0.124)PROT25 0.041 (0.569)PROT50 1.294*** (0.097)INVASIVE 1.932*** (0.117)

Fixed coefficientCOST −0.063*** (0.003)Log-likelihood −4703Observations 17,514Respondents 973Halton draws 5000Adjusted ρ2 0.262BIC 9500

*** and ** indicate 1%- and 5%-level of confidence, respectively. Adjusted ρ2 is computed asρ2 = 1 − (LLm − k)/LL0, where LLm and LL0 are the log-likelihoods of the full model, andthe intercept-onlymodel respectively, and k the number of parameters. Bayesian Informa-tion Criterion (BIC) is calculated as BIC = −2LLm + k ⋅ ln(N) with N denoting the num-ber of respondents. The use of BIC is preferred to Akaike Information Criterion because itimposes a stronger penalty on the inclusion of more parameters in the model.

237T. Börger et al. / Ecological Economics 108 (2014) 229–241

preferences by means of the choice tasks presented to them despitethe low level of prior knowledge of the topic reported earlier. Beyondthat, the level of choice certainty summed up for all six choice tasks(CERTAIN) did not significantly affect choice probabilities in the mixedlogit models 1 to 3.

The level of education of the respondent (approximated by thedummy variable UNI) did not show any significant effect on choices inmodels 1 to 3. There were no effects of respondents' age (AGE) andhousehold income (INCOME) on choices. Similarly, there was no influ-ence of respondents' previous experience of sea angling (ANGLING) orscuba diving (SCUBA), professional experience in any marine sector(MARSEC) or their distance from the Dogger Bank (DIST_DB). Alterna-tive distance variables to the North Sea and the nearest coast, as wellas the natural logarithm of the three different distance variables weretested, however, none of them had an impact on choices wheninteracted with ASC_CHANGE. In an alternative model, dummy vari-ables indicating respondents that live within 1, 2, 4, 6, 10, 25 and50 miles from a coastline were interacted with ASC_CHANGE, butagain there were no significant effects of these variables on choices.

In model 2, a set of dummy variables drawn from attitudinal ques-tions, some of which were also used to identify protest respondents,9

were added. All significantly influence choices. Coefficients of state-ments adverse to the Dogger Bank management plan (NOTNEC) andits financing (BURDEN, ENOUGH)were negative and remain so after in-cluding further variables in model 3. This means that respondentsagreeing to the statements “I think a Dogger Bank management planis not necessary” (NOTNEC), “Taxes and fees are already too high, sothere should not be an additional financial burden” (BURDEN) and “I al-ready pay enough for other things” (ENOUGH) had a lower probabilityof choosing one of themanagement options. The effect of a fourth state-ment critical of the financing of the management plan reading “The

9 While only those respondents agreeing to these statements and choosing the nochange option in every choice task had been identified as protesters, these variables areused to test the influence of those attitudes towards the proposedmanagementmeasuresand its provision mechanism on choices.

government should cut public spending on other things instead ofexpecting a contribution from me” (CUT) was significantly positive inmodels 2 and 3. In contrast, respondents agreeing to the statements“I enjoy contributing to a good cause no matter what it is” (ENJOY) or“I think it is better to ask experts whether or not to carry out this DoggerBank management plan” (EXPERTS) were more likely to prefer a costlymanagement option over the no-change (zero cost) option.

Model 3 included further attitudinal variables, which are related tospecific attributes and were therefore interacted with these attributesrather than with ASC_CHANGE. Results in Table 6 show that respon-dents agreeingwith the statement “Without amanagement plan the di-versity of species on the Dogger Bank will continue to decrease”(SPECDIV) had a stronger preference for species diversity. The coeffi-cients of the main effects of SPEC10 and SPEC30 (Table 6) were not sig-nificant in model 3. While respondents who agreed with the statement(SPECDIV) preferred higher species diversity more strongly, choices ofrespondentswho did not agreewith this statementwere not influencedby this attribute (i.e. indifferent to changes in species diversity). In amodel not reported here, choices of respondents who did not agree toSPECDIV were completely indifferent to changes in species diversity.This supports the finding that agreement to this attitudinal statementis a valid predictor of preferences for the respective benefits. Respon-dents agreeing to “Porpoises, seals and seabirds need protectingthrough the management plan” (PROTECT) valued this particularfeature of the plan significantly more than respondents not holdingthis view.10 Preferences for a reduction of the spread of invasivespecies (INVASIVE) were not influenced by the inclusion of theseinteractions.

In all of the reportedmodels, themodelfit to the datawas very good.Louviere et al. (2000) state that adjusted McFadden's ρ2 between 0.2and 0.4 indicate extremely good model fit for this type of logit model.Overall,modelfit improved frommodel 1 through tomodel 3 as indicat-ed by an increasing adjusted ρ2 and a decreasing BIC. The inclusion of at-titudinal variables in models 2 and 3 further increased the predictivepower of the model and thus better explained stated choices.

4. Discussion, Policy Implications and Concluding Remarks

The main focus of this study was to test the applicability of DCE forthe valuation of benefits arising from the management of an offshoreMPA.While there are a very limitednumber of stated preference studiesrelating to offshore sites, this is one of the few studies to explicitly linkthe valuation scenarios to current marine management discussions.This particular approach includes a set of largely quantifiable attributespecifications that can be directly linked to real-world managementmeasures. Establishing these links and conveying them in a credibleand readily understandable way in the valuation scenarios provedchallengingwith respect to an offshoremarine site. Yet results from val-uation studies of this kind can be most informative to marine manage-ment if the link between proposed management measures, ensuingenvironmental change, and the assessment of the resulting benefits isas clear and direct as possible (McVittie and Moran, 2010).

4.1. Level of Concern of the General Public

The importance of the coastal and marine environment to an islandnation such as the UK is reflected in the finding that a large majority ofrespondents have visited and/or travelled across theNorth Sea. As antic-ipated this study found low levels of self-rated knowledge about the

10 In anothermodel not reported here, interactions of statements SPECDIV and PROTECTwith ASC_CHANGE yielded significantly positive coefficients indicating a higher WTP ofsupporters of these statements for all attributes. However, the coefficients of the main ef-fects (SPEC10, SPEC25, PROT25 sand PROT50) were still significantly positive. Conse-quently, these statements need to be included in interaction with their respectiveattributes to detect different effects on choices.

Table 6Mixed logit models with interactions.

Model 1 Model 2 Model 3

Coefficient Std. Err. Coefficient Std. Err. Coefficient Std. Err.

Mean of random coefficientsASC_CHANGE −0.060 (0.716) 0.318 (0.685) 0.882 (0.659)SPEC10 0.258** (0.112) 0.249** (0.112) −0.127 (0.161)SPEC25 0.495*** (0.087) 0.495*** (0.086) 0.069 (0.150)PROT25 1.541*** (0.098) 1.540*** (0.098) 0.972*** (0.153)PROT50 1.967*** (0.115) 1.973*** (0.114) 1.167*** (0.164)INVASIVE −1.675*** (0.109) −1.681*** (0.110) −1.694*** (0.111)

Standard deviation of random coefficientsASC_CHANGE 2.377*** (0.118) 2.115*** (0.111) 1.987*** (0.108)SPEC10 0.564** (0.222) 0.621*** (0.198) 0.568*** (0.216)SPEC25 0.863*** (0.124) 0.848*** (0.125) 0.875*** (0.124)PROT25 0.044 (0.359) 0.122 (0.405) 0.215 (0.334)PROT50 1.325*** (0.099) 1.300*** (0.100) 1.352*** (0.102)INVASIVE 2.023*** (0.122) 2.039*** (0.121) 2.082*** (0.122)

Non-random coefficientsCOST −0.065*** (0.003) −0.065*** (0.003) −0.066*** (0.003)AGEa 0.007 (0.007) 0.008 (0.006) 0.006 (0.006)MALEa −0.530** (0.212) −0.573*** (0.198) −0.476** (0.190)UNIa −0.119 (0.213) −0.271 (0.199) −0.283 (0.191)INCOMEa 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)HHSIZEa 0.000 (0.082) 0.002 (0.077) 0.014 (0.074)ENVORGa 1.289*** (0.238) 0.930*** (0.223) 0.866*** (0.212)ANGLINGa 0.130 (0.263) 0.059 (0.243) −0.061 (0.233)SCUBAa −0.005 (0.280) 0.082 (0.258) 0.127 (0.248)FERRYa 0.446** (0.213) 0.287 (0.198) 0.211 (0.189)FLIGHTa 0.455** (0.210) 0.382** (0.195) 0.353* (0.187)MARSECa −0.580 (0.530) −0.470 (0.500) −0.440 (0.474)DIST_DBa 0.001 (0.001) 0.001 (0.001) 0.000 (0.001)CERTAINa 0.021 (0.020) 0.004 (0.019) −0.005 (0.018)NOTNECa −0.741** (0.340) −0.534* (0.322)BURDENa −0.886*** (0.227) −0.821*** (0.218)ENOUGHa −0.755*** (0.225) −0.749*** (0.216)CUTa 0.504** (0.204) 0.400** (0.196)ENJOYa 1.392*** (0.205) 1.188*** (0.198)EXPERTSa 0.693*** (0.195) 0.467** (0.190)SPECDIV*SPEC10 0.545*** (0.171)SPECDIV*SPEC25 0.602*** (0.169)PROTECT*PROT25 0.714*** (0.154)PROTECT*PROT50 1.102*** (0.170)Log-likelihood −4548 −4481 −4446Observations 17,118 17,118 17,118Halton draws 1000 1000 1000Adjusted ρ2 0.267 277 0.282BIC 9274 9100 9085

***, ** and * indicate 1%-, 5%- and 10%-level of confidence, respectively. The top sections of the columns reportmean estimates of attribute coefficients modelled as random. Adjusted ρ2 iscomputed as ρ2 = 1 − (LLm − k)/LL0, where LLm and LL0 are the log-likelihoods of the full model, and the intercept-only model respectively, and k the number of parameters. BayesianInformation Criterion (BIC) is calculated as BIC = −2LLm + k ⋅ ln(N) with N denoting the number of respondents. The use of BIC is preferred to Akaike Information Criterion because itimposes a stronger penalty on the inclusion of more parameters in the model.

a Interacted with ASC_CHANGE.

238 T. Börger et al. / Ecological Economics 108 (2014) 229–241

case study site and its management, an observation also made for thecase of the deep-sea by Jobstvogt et al. (2014). Nevertheless, followingthe provision of limited information on the implications ofmanagementof the main commercial activities on the Dogger Bank, support for theirregulation was found. This provides evidence of the level of concernof the general public regarding the consequences of these offshoreactivities and supports the approach taken in the subsequent choice ex-periment. Nevertheless, these findings should be interpreted with cau-tion due to the potential influence of the provided information onresponses.

4.2. Social Benefits of the Proposed Management Plans

Results further show that the UK public hold significant values forenvironmental benefits generated by conservation measures in an off-shore location. The elicited values obtained through this study corre-spond to real-world management options that are currently underdiscussion for an offshore MPA in the UK. Regarding the specific choice

attribute values, protection of charismatic species (porpoises, seals andseabirds) on 25%of theUK section of the Dogger Bank is proportionatelygreater than for protection on 50% of the area. This non-linear increase isimportant given the disagreement over such protection between thefishing industry and the ENGOs who advocate for these respective pro-tection levels. Similarly, WTP for increasing species diversity by 25%resulting from the exclusion of bottom trawling from 50% of the UK sec-tion of the Dogger Bank is higher than for an increase of only 10%, if bot-tom trawlingwas excluded on just 25% of the area, although again thereis no linear relationship. Comparing these two attributes, values for theprotection of charismatic species exceed those for general species diver-sity. This supports findings elsewhere that suggest that respondents arecapable of distinguishing between different (quantitative) levels of spe-cies protection but that they react more strongly to charismatic speciescompared to general diversity (Martín-López et al., 2007; Jacobsen et al.,2008). However, this might depend on the cultural background of re-spondents (Ressurreição et al., 2012). A confounding issue in this surveycould result from the link suggested between the regulation of two

239T. Börger et al. / Ecological Economics 108 (2014) 229–241

commercial sectors on the Dogger Bank and the resulting environmen-tal changes. While the choice tasks focus specifically on the differentlevels of environmental changes, it is conceivable that some respon-dents' choices were motivated by the underlying restrictions to fishingand wind farm development. This ambiguity often occurs when basingvaluation scenarios on real-world management decisions where envi-ronmental improvements may come at the expense of economic or so-cial factors.

4.3. Validity of the Elicited Values

Despite respondents' low levels of prior knowledge of the study siteand its characteristics there are several indicators supporting the valid-ity of the elicited values. Self-reported choice certainty, as assessed aftereach choice task, was found to be high. This can be considered an indi-cation that the information provided was sufficient for respondents tolink the scenarios to their existing attitudes, and consequently to statechoices based on individual preferences. Choice certainty, however,does not affect choices indicating no systematic difference betweenthe preference structures of respondents who feel certain about theirchoices and those who are less certain.

The validity of the elicited values is further supported because pref-erences for the proposed benefits are predicted by certain respondentcharacteristics in a plausible manner. The higher likelihood of respon-dents who have seen the North Sea (from a flight or ferry) to preferone of the management options indicates that familiarity with thegood or some feeling of connection (but not direct use) increases thebenefits to be expected from the proposed measures. Similarly, mem-bers of environmental organisations value the proposed measuresmore than non-members. This effect is frequently found in studies val-uing biodiversity (e.g. Jobstvogt et al., 2014; Yao et al., 2014). Indicatorsof direct use of the marine environment, such as recreational anglingand scuba diving or professional experience in a marine sector do notsystematically affect choices. This is in contrast to the results ofJobstvogt et al. (2014) with respect to deep-sea ecosystem services.11

The findings of the present study therefore indicate that values for theproposed benefits are mostly non-use in nature. This interpretation isfurther supported by the lack of any distance effects. Attitudes towardsthe management plan and recognition of existing financial burdens forhouseholds influence choices and WTP mostly as expected.

In addition to preference heterogeneity explained by these attitudi-nal variables, there is evidence of further random heterogeneity in pref-erences for the proposed benefits. This heterogeneity is particularlystrong for changes in species diversity and the spread of invasive spe-cies, the two attributes which are likely to be more difficult to under-stand. Consistent with this, the results indicate rather uniformpreferences for the protection of some charismatic species on 25% ofthe UK sector of the Dogger Bank— a benefit potentiallymore easily un-derstood by respondents.

The failure to detect any decreasing or increasing distance effect inthe choice data is interesting against the backdrop of ongoing discus-sions relating to distance decay of values elicited in stated preferencesurveys. Previous studies have found evidence for distance decay foruse and non-use values in terrestrial (Bateman et al., 2006) and coastalsettings (Luisetti et al., 2011). Schaafsma et al. (2012) have furthershown that distance decay with respect to a terrestrial environmentalgood also depends on the availability of substitutes, which might differbetween different geographical directions from the good to be valued.The present study, however, does notfind evidence for any of those spa-tial patterns in values for Dogger Bank ecosystem services. Nor are there

11 As one referee pointed out one choice attribute in Jobstvogt et al. (2014) relates to fu-ture use, which might explain the effect of angling on such values. Moreover, the fact thatthis study was conducted as a postal survey might have led to a high number of activeusers of the marine environment in the sample, which might be another explanation ofthat effect.

differences between respondents living near the coast (and potentiallyhaving substitutes for the proposed benefits available) and further in-land. In an island nation such as the UK it is conceivable that respon-dents living at another part of the coast value the benefits fromconservation of the Dogger Bank differently because (some of) thesebenefits are provided by the marine environment on their doorstep.Yet such differences could not be found. These findings reflect those re-ported by Rolfe andWindle (2012) and Choi (2013). They indicate thatconsiderations about distance effects found for terrestrial and coastalenvironmental goods do not translate to an offshore site, such as theDogger Bank. Non-use values, which constitute the major motivationfor the benefits assessed in this study, might not exhibit linear distanceeffects but instead could show other spatial patterns (Johnston et al.,2011). It is possible that respondents living close to each other exhibitlocal clusters of relatively high or low WTP. Further research in thisarea is needed to clarify these preference patterns for marine environ-mental goods and services.

Finally two further caveats require mention. The first concernsvalues for species protection. There is still debate about how to includethe conservation status of species (Jacobsen et al., 2008). This surveyavoided explicit mentioning of conservation status of the harbour por-poise because it is not one of the features that led to the designationof the UK part of the Dogger Bank as a cSAC. Nevertheless conservationvaluemay be an important component of non-use value. Secondly, stan-dard survey practice requires the description of thepaymentmode to beas plausible as possible. The present survey did not further specify thehousehold tax. Reasons for this are mentioned earlier, however, it can-not be said with certainty that this did not diminish the perceived real-ism of the valuation scenarios. The results should therefore beinterpreted against this background.

Despite thementioned limitations, an online survey allowed respon-dents from across the UK to be sampled. The resulting sample reflectsthe general structure of the UK population in terms of certain demo-graphic characteristics. This is crucial when valuation estimates are tobe used in an environmental CBA to support decision-making. It hasalso been shown that WTP estimates from postal and online surveysare comparable (Olsen, 2009; Rolfe andWindle, 2011). However, onlinesurveys offer limited control on how respondents complete the ques-tionnaire and take in the information. Christie and Rayment (2012)have shown that in workshop settings complex valuation topics cansuccessfully be conveyed to respondents, it is not clear whether this isalso true for online surveys. Most variables in the regression models af-fect choices in a plausible way, but it remains unclear towhat extent re-spondents really grasp the concepts of species diversity and invasivespecies. Proper testing of questionnaires such as the use of “think-aloud” interviews proved to be crucial in this respect. It should also bestressed that the use of pictograms to increase understanding of choiceattributes is not undisputed. While such pictograms ease the cognitiveburden for respondents, they may cause emotional reactions in the re-spondent that might bias choices but are unobservable to the analyst.

4.4. Implications for Marine Management and Policy

In terms of marine management, the Dogger Bank provides a casestudy of a transnationally governed offshore MPA. Although there arelikely to be additional cross-border benefits of consistent managementof the MPAs by respective Member States, these were not assessed inthe present survey but should be incorporated into future studies. Nev-ertheless results of studies such as this one can inform bio-economicmodels used to identify optimal locations for offshore wind farms orMPAs. For example, the model developed by Punt et al. (2009) deter-mines the optimal location of a wind farm in the Dutch EEZ givenlocal physical conditions and impact on ecosystem services but doesnot take into account the changes in values resulting from the ecologicalchanges. Börger et al. (2014) have argued that valuation is indispens-able for the incorporation of ecosystem services into such marine

240 T. Börger et al. / Ecological Economics 108 (2014) 229–241

planning efforts as required by recent legislation, such as the EUMarineStrategy Framework Directive (Bertram et al., 2014) or in a less explicitmanner by the US National Ocean Policy (NOC, 2013).

4.5. Concluding Remarks

The survey clearly identifies the conservation benefits from an off-shore MPA for the general public. Through the use of a DCE, the presentstudy has significance beyond this particular case. It demonstrates theapplicability of stated preference methods to inform detailed policy-making for offshore sites. Despite limited prior knowledge about thestudy site, survey respondents are able to develop and express prefer-ences on specific attributes based on the information provided in thesurvey. Studies of this kind can be employed to assess the attainmentof potential benefits from marine management by the general public,a group beyond the immediate stakeholders and direct marineuser communities. In negotiations about marine management plans,valuations can inform negotiations by providing the wider societal per-spective and, thus, help to establish priorities among conflicting man-agement goals.

Acknowledgements

The research leading to these results has received funding from theEuropeanUnion's Seventh Framework Programme for research, techno-logical development and demonstration (FP7/2007-2013) within theOcean of Tomorrow call under Grant Agreement No.266445 for the pro-ject Vectors of Change in Oceans and Seas Marine Life, Impact on Eco-nomic Sectors (VECTORS). We would like to thank Shona Thompson(IECS, University of Hull) for producing the maps (Figs. 1 and 3) andfor generating the distance variables using GIS. We are grateful to thetwo anonymous referees for their comments and suggestions thathave improved the paper.

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